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Battery Machine Learning Jobs in Seattle, WA (NOW HIRING)

Amazon

Applied Science Manager, Prime Air

Seattle, WA · On-site

You will bridge the gap between Geospatial Intelligence and Machine Learning to revolutionize our ... Drive the development of high-scale scheduling systems that manage battery life, maintenance cycles ...

Amazon

Applied Science Manager, Prime Air

Seattle, WA · On-site

You will bridge the gap between Geospatial Intelligence and Machine Learning to revolutionize our ... Drive the development of high-scale scheduling systems that manage battery life, maintenance cycles ...

Amazon

Applied Science Manager, Prime Air

Seattle, WA · On-site

You will bridge the gap between Geospatial Intelligence and Machine Learning to revolutionize our ... Drive the development of high-scale scheduling systems that manage battery life, maintenance cycles ...

Terex Corporation

Design Engineer II/Sr

Redmond, WA · On-site

$91K - $140K/yr

Experience with battery qualification and machine integration involving advanced battery ... Learning. * For more information on why Terex is a great place to work click on the link ! Careers ...

Terex

Design Engineer II/Sr

Redmond, WA · On-site +1

$91K - $140K/yr

Experience with battery qualification and machine integration involving advanced battery ... Learning. * For more information on why Terex is a great place to work click on the link ! Careers ...

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Battery Machine Learning information

What is battery machine learning?

Battery machine learning involves the application of machine learning algorithms to analyze, predict, and optimize the performance, lifespan, and safety of batteries. Professionals in this field work on developing data-driven models to forecast battery degradation, enhance energy management systems, and improve battery design. Their work is crucial in sectors such as electric vehicles, renewable energy storage, and consumer electronics, where battery efficiency and reliability are key. By leveraging large datasets from battery usage and testing, they help accelerate innovation and reduce costs in battery technology.

What are the key skills and qualifications needed to thrive as a battery machine learning engineer?

To thrive as a Battery Machine Learning Engineer, you need a strong background in machine learning, data analysis, and battery science, typically supported by a degree in engineering, computer science, or a related field. Familiarity with Python, TensorFlow or PyTorch, data processing tools, and battery management system (BMS) software is highly valued. Strong problem-solving skills, collaboration, and effective communication set standout professionals apart in this role. These skills are essential to develop accurate predictive models that optimize battery performance and longevity, driving innovation in energy storage technologies.

What are some common challenges faced by professionals working in battery machine learning roles?

Professionals in Battery Machine Learning often encounter challenges related to limited or noisy datasets, as battery performance data can be expensive and time-consuming to collect. Additionally, integrating domain knowledge from electrochemistry with advanced machine learning techniques requires strong interdisciplinary collaboration. Staying up-to-date with both the latest AI methods and battery technology advancements is essential but can be demanding. Collaborating closely with researchers, engineers, and data scientists is a key aspect of the role, as projects frequently depend on cross-functional teamwork to translate predictive insights into practical battery innovations.

What are popular job titles related to Battery Machine Learning jobs in Seattle, WA?

For Battery Machine Learning jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Battery Machine Learning jobs in Seattle, WA look for?

The top searched job categories for Battery Machine Learning jobs in Seattle, WA are:

Battery Machine Learning jobs near you
Infographic showing various Battery Machine Learning job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer - On-Device Adaptive Control

Seattle, WA • On-site

Socket.dev
Network Security • 1 - 10 employees
Learn more about Socket.devexternal

$150 - $190/hr

Other

Posted 3 days ago

New

Job description

The Energy Tech org builds systems for managing the energy flow and thermals of Apple devices in service of a great user experience. Within this org, the team develops end-to-end solutions utilizing on-device machine learning and control, creating new techniques from data analysis and prototyping. Our work directly impacts the behavior of Apple devices across the product families.

Description

We are developing on-device control systems that manage thermal and energy tradeoffs on Apple devices. This means building models that capture device dynamics, designing cost functions that encode explicit priorities, and shipping control loops that adapt to real-world conditions. We're looking for a Machine Learning Engineer who can work across the full stack: analyzing field data to understand device behavior, prototyping control and ML algorithms, and getting them running on-device. The problems are messy — noisy sensors, changing hardware, competing objectives — and the solutions need to be simple enough to ship on constrained hardware.

Minimum Qualifications
  • MS or PhD in controls, robotics, electrical engineering, computer science, or related field — or BS with relevant experience
  • Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)
  • Strong programming skills in Python; comfort with C/C++ for on-device work
  • Experience working with real-world sensor data (noisy, incomplete, high-volume)
  • Demonstrated ability to take a project from data exploration through working prototype
Preferred Qualifications
  • Experience with thermal systems, battery management, or energy optimization
  • Familiarity with embedded or resource-constrained environments
  • Background in system identification or online parameter estimation
  • Comfort with ambiguity — able to scope and drive work without detailed specifications
  • Track record of shipping models or control systems into production, not just research
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